Semi-overcomplete convolutional auto-encoder embedding as shape priors for deep vessel segmentation
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arXiv
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| Format: | Preprint |
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2024
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| author | Sadikine, Amine Badic, Bogdan Tasu, Jean-Pierre Noblet, Vincent Visvikis, Dimitris Conze, Pierre-Henri |
| author_facet | Sadikine, Amine Badic, Bogdan Tasu, Jean-Pierre Noblet, Vincent Visvikis, Dimitris Conze, Pierre-Henri |
| contents | The extraction of blood vessels has recently experienced a widespread interest in medical image analysis. Automatic vessel segmentation is highly desirable to guide clinicians in computer-assisted diagnosis, therapy or surgical planning. Despite a good ability to extract large anatomical structures, the capacity of U-Net inspired architectures to automatically delineate vascular systems remains a major issue, especially given the scarcity of existing datasets. In this paper, we present a novel approach that integrates into deep segmentation shape priors from a Semi-Overcomplete Convolutional Auto-Encoder (S-OCAE) embedding. Compared to standard Convolutional Auto-Encoders (CAE), it exploits an over-complete branch that projects data onto higher dimensions to better characterize tiny structures. Experiments on retinal and liver vessel extraction, respectively performed on publicly-available DRIVE and 3D-IRCADb datasets, highlight the effectiveness of our method compared to U-Net trained without and with shape priors from a traditional CAE. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13001 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Semi-overcomplete convolutional auto-encoder embedding as shape priors for deep vessel segmentation Sadikine, Amine Badic, Bogdan Tasu, Jean-Pierre Noblet, Vincent Visvikis, Dimitris Conze, Pierre-Henri Image and Video Processing Computer Vision and Pattern Recognition Machine Learning The extraction of blood vessels has recently experienced a widespread interest in medical image analysis. Automatic vessel segmentation is highly desirable to guide clinicians in computer-assisted diagnosis, therapy or surgical planning. Despite a good ability to extract large anatomical structures, the capacity of U-Net inspired architectures to automatically delineate vascular systems remains a major issue, especially given the scarcity of existing datasets. In this paper, we present a novel approach that integrates into deep segmentation shape priors from a Semi-Overcomplete Convolutional Auto-Encoder (S-OCAE) embedding. Compared to standard Convolutional Auto-Encoders (CAE), it exploits an over-complete branch that projects data onto higher dimensions to better characterize tiny structures. Experiments on retinal and liver vessel extraction, respectively performed on publicly-available DRIVE and 3D-IRCADb datasets, highlight the effectiveness of our method compared to U-Net trained without and with shape priors from a traditional CAE. |
| title | Semi-overcomplete convolutional auto-encoder embedding as shape priors for deep vessel segmentation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2409.13001 |